COMBINING PROPOFOL AND REMIFENTANIL … · combining propofol and remifentanil pharmacokinetic and pharmacodynamic models in the operating room: an observational study by farrant

COMBINING PROPOFOL AND REMIFENTANIL PHARMACOKINETIC

AND PHARMACODYNAMIC MODELS IN THE OPERATING ROOM:

AN OBSERVATIONAL STUDY

by

Farrant Hiroshi Sakaguchi

A thesis submitted to the faculty of

The University of Utah

in partial fulfillment of the requirements for the degree of

Master of Science

Department of Bioengineering

The University of Utah

December 2004

2

Copyright © Farrant Hiroshi Sakaguchi 2004

All Rights Reserved

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Supervisory Committee Approval Form

4

Final Reading Approval Form

ABSTRACT

Remifentanil and propofol are commonly used together for total intravenous

anesthesia. Though their synergistic pharmacodynamic interaction has been

characterized with surrogate measures in volunteers, the relationship of these surrogate

measures to actual surgical stimuli has not been validated prospectively in the operating

room. This study combines a set of propofol and remifentanil pharmacokinetic (PK) and

pharmacodynamic (PD) models to estimate their PD interaction and predicts the

resulting likelihood of sedation and analgesia intraoperatively.

With IRB approval and informed consent, we studied 24 ASA physical status I, II,

and III patients scheduled for laproscopic surgery receiving total intravenous anesthesia.

Standard anesthetic practice was not altered for this study. Responses and non-

responses to the intraoperative stimuli of laryngoscopy and skin incision were recorded.

The predicted effect-site concentrations at these data points, and at the loss and return of

responsiveness, were plotted on response-surface models for corresponding surrogate

measures determined in volunteers. Patient observations were compared to

pharmacodynamic predictions. Methods to reduce differences between the model

predictions and observations in the patients are identified and discussed.

The results of this study suggest that tracheal intubation, a surgical milestone, is

more stimulating than the surrogate measure of laryngoscopy alone. The PK-PD

v

combined models for a surrogate indicator of sedation (OAA/S < 2) predict loss of

responsiveness (LOR) and recovery of responsiveness (ROR) for 35% and 87% of the

patients above the 50% isobol, respectively. The data also suggest that propofol, rather

than remifentanil, is the main contributor to responsiveness in these patients. Clinically,

this may mean that a quick recovery of consciousness may be achieved while managing

postoperative pain by maintaining opioid levels while propofol levels are reduced.

TABLE OF CONTENTS

ABSTRACT..............................................................................................................................iv

LIST OF FIGURES................................................................................................................. vii

ACKNOWLEDGMENTS..................................................................................................... viii

Chapter

1. INTRODUCTION........................................................................................................... 1

Purpose of Study ...................................................................................................... 1

Pharmacological Modeling ...................................................................................... 2

Methods for Preliminary Study............................................................................. 10

Conclusion from Preliminary Study ..................................................................... 11

References ............................................................................................................... 13

2. OBSERVATIONAL STUDY.......................................................................................... 15

Introduction ............................................................................................................ 15

Methods................................................................................................................... 16

Results ..................................................................................................................... 24

Discussion ............................................................................................................... 32

References ............................................................................................................... 37

3. CONCLUSION ............................................................................................................. 40

Summary................................................................................................................. 40

Comparison of Observational Studies and Clinical Studies ............................... 40

Utility and Limitations of Clinical Pharmacological Modeling .......................... 41

Future Work............................................................................................................ 42

References ............................................................................................................... 43

vii

LIST OF FIGURES

Figure Page

1.1. Three compartment model with an effect-site. ...................................................3

1.2. Pharmacodynamic Emax models for sedation and laryngoscopy.....................5

1.3. Isobologram for three pharmacodynamic interactions ......................................6

1.4. Response surface models for surrogate measures from Kern et al....................9

2.1. Ceff values at loss of responsiveness on the sedation response surface

(OAA/S<2) ............................................................................................................ 27

2.2. Ceff values at recovery of responsiveness on the sedation response surface

(OAA/S<2) ............................................................................................................ 28

2.2 Ceff values at recovery of responsiveness on the sedation response surface

2.3 Ceff values at laryngoscopy followed by tracheal intubation on the response

surface for laryngoscopy..................................................................................... 29

2.4 Ceff values at the first skin incision on the response surface for shin algometry

............................................................................................................................... 30

2.5 Ceff values at the first skin incision on the response surface for electrical tetany

............................................................................................................................... 31

ACKNOWLEDGMENTS

I would like to express appreciation to Dr. Dwayne Westenskow for his support and

encouragement throughout this project. I am indebted to Dr. Steve Kern for his high

expectations and trust in my abilities. I am grateful to Dr. Kenneth Horch for teaching

me to think rationally and to expect more of myself while progressing in life. I

appreciate Dr. Talmage Egan’s constant enthusiasm and clinical insights. I thank Noah

Syroid for his support in the project, help and patience with my coding. I also

acknowledge the support and help of numerous friends who have encouraged, helped,

and at times, mocked me through this process. I thank my parents, Maisie and Douglas

Sakaguchi, for their continual love, trust, support, encouragement, and teaching. I

especially thank them for their examples of seeking after wisdom and excellence in

every area of life while teaching what is of greatest value. I thank my God for being

alive and for surrounding me with such fine mentors, colleagues, friends, and family.

This research has been generously funded by the NIH Grant # 1 RO1 HL 64590

and by the NASA Rocky Mountain Space Consortium. Thank you to MedFusion for the

use of the Medex 3010a continuous infusion pumps. We appreciate the support of Colin

Corporation for the use of their Colin CBM-7000, a continuous, non-invasive blood

pressure monitor.

CHAPTER 1

INTRODUCTION

Purpose of Study

Pharmacodynamic studies are often used to characterize the concentration-effect

relationship of a single drug.1,2,3 Predicting the effect of two drugs that have a

pharmacodynamic interaction is complex. As a result of this complexity,

pharmacodynamic interaction studies are usually performed in volunteers in a

controlled environment.4,5,6,7,8,9 The most significant limitation of these volunteer studies

is that responses to surrogate measures of surgical stimuli are used. The relationship

between the stimulus induced by a surrogate measure, such as electrical tetany, and by a

surgical measure, such as skin incision, remains unclear. A volunteer study also

evaluates sedation differently than in the perioperative setting; a volunteer study often

describes the depth of sedation using a graded scale such as the observer’s assessment of

alertness/sedation (OAA/S).4,10 In the operating room “unconsciousness” is simply

observed when the patient is non-responsive to verbal commands. Additionally, the

volunteer study rigorously controls the dosing regimen over wide concentration ranges

and allows time for the plasma concentration to equilibrate with the effect-site

concentration.

2

This study combines pharmacokinetic and pharmacodynamic models,

comparing these predictions with observations in patients. The goal was to assess

models developed in volunteers by Kern et al.4,5 by pharmacodynamically relating

surgical stimuli to surrogate measures. An observational study has several limitations.

The first is that the dosing regimen is not strictly controlled, resulting in periods of non-

steady-state kinetics and greater uncertainty with respect to drug concentrations in the

brain. Secondly, for ethical reasons, surgical stimuli are not attempted at low drug

concentrations. Nor are they repeated without clinical expedience. Thus, for each

surgical milestone, only a single data point was used from each patient.

Pharmacological Modeling

A pharmacokinetic (PK) model describes the changing concentration of a drug in

the body over time after a dose is administered; pharmacokinetics describe what the

body does to the drug.11 Figure 1.1 diagrams a three-compartment model with an effect-

site compartment used to describe the distribution of drugs through different tissues.11, 12

These theoretical, nonphysical compartments represent different tissues. Once a drug is

administered, it is transported in the blood to different compartments, including the

biophase or effect site.12 The biophase consists of the specific tissues, membranes,

receptors, and/or enzymes where the drug exerts its pharmacologic effect; the central

nervous system is considered the biophase for general anesthetics.12 Thus, although

plasma concentrations of an anesthetic agent are relatively easy to obtain, they are of less

direct interest than the effect-site concentrations (Ceff).13 The transport of drugs

3

Figure 1.1. Three compartment model with an effect-site. Drug doses given

intravenously via infusion or bolus enter the central compartment (roughly the

circulatory system). The drug is then distributed to different tissue types or

compartments. The effect-site is where the drug exerts its pharmacological effect.

Pharmacokinetic models predict the drug concentrations in each compartment.

4

between compartments is generally described by first order differential equations.14

A pharmacodynamic (PD) model describes the effect of the drug on the patient as

the concentration changes; pharmacodynamics describe the drug effects as functions of

the drug concentrations at the effect-site.11 The Emax model, Equation 1.1, is a common

PD model for anesthetics and describes a concentration-response relationship that is

sigmoidal in shape (Figure 1.2).15

1γ)

50Cntration/E(DrugConce

γ)

50Cntration/E(DrugConce

EffectNormalized+

= [1.1]

This s-shaped curve is characterized by the EC50 and by γ (the steepness). At the EC50

concentration, there is a 50% probability that the patient is “adequately anesthetized.”4,15

Anesthesia is generally targeted at EC95 concentrations such that there is a 95% or higher

probability that patients will not respond. In most patients, higher anesthetic

concentrations will have minimal additional pharmacodynamic benefits.

When more than one anesthetic is used, interactions can produce several positive

effects.15,16,17 For example, a certain concentration of either Drug A or Drug B (points J

and K in Figure 1.3) may prevent a response to a painful stimulus. The two drugs can

also be used in combination to achieve the same drug effect. The curves that connect

points j and k and describe combinations of the drugs that predict equal drug effect are

termed isoboles. The shape of these isoboles depends on the pharmacodynamic

interaction of Drugs A and B. Three potential interactions (synergy, additivity, and

5

0 2 4 6 8 10 12 14 160%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Drug Concentration

Like

lihoo

d of

Dru

g E

ffec

t

SedationLaryngoscopy

Sedation LaryngoscopyEC50 EC95 EC50

50% Drug Effect

95% Drug Effect

Figure 1.2. Pharmacodynamic Emax models for sedation and laryngoscopy. In this

figure, the likelihood of drug effect is a function of drug concentration. The EC50 and

EC95 describe the drug concentrations necessary to achieve 50% and 95% of drug

effect, respectively.

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0 0.5 10

0.5

1

Normalized Drug A Concentration

Nor

mal

ized

Dru

g B

Con

cent

ratio

n

Synergistic Additive AntagonisticInteraction Interaction Interaction

X Y Z

J

K

Figure 1.3. Isobologram for three pharmacodynamic interactions. The points at J and

K represent drug effect when either Drug A or Drug B are given alone (i.e. the 50%

likelihood of drug effect). The solid lines represent the combination of drug

concentration pairs necessary to achieve the same effect level for different

pharmacodynamic interactions. In this figure, the points X, Y, and Z are at equal

levels of drug effect, depending on the interaction. Depending on the interaction, for

a fixed concentration of Drug B, different concentrations of Drug A are necessary to

achieve the same drug effect.

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antagonism) are shown in Figure 1.3.15,18

Synergism results in reduced individual drug concentrations while providing a

targeted effect-level. Additivity means there is no interaction between the two drugs.

Antagonism, in contrast to synergism, requires increased drug concentrations to provide

a targeted effect-level. A collection of isoboles, where curves are shown for a range of

effect-levels, can be interpolated to create a response surface; a response surface

represents the full range of probabilities of a drug effect for different drug concentration

pairs.4,6,9,15,18

Pharmacokinetic and pharmacodynamic models can be combined to describe the

effect of a drug over time.11 There are several challenges however, due to assumptions

made by PK and PD models. PK models assume that a drug distributes homogenously

and instantaneously within each compartment. The true complexity of intravascular

mixing and drug transport is ignored.19,20 For example, the predicted Ceff can rise the

moment a drug is administered despite that this immediate rise in Ceff does not make

physiological sense for anesthetics acting in the CNS. Few anesthetic models consider

the effects of temperature, cardiac output, recirculation and the varying distribution

volumes over time.19,20 Anesthetic PD models are also misspecified by using a

continuous function to describe logistic observations of “adequate anesthesia” relative to

a given stimulus. Most PD models describe the probability of the drug moderating a

noxious stimulus instead of the physiological action of the anesthetic.20 Despite these

weaknesses, combined PK-PD models may be useful tools for anesthesiologists to

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predict the rate of onset of drug effect, the duration of the drug effect, and the minimum

effective dose.4,11

Real-time visualization of drug pharmacokinetics and pharmacodynamics may

help anesthesiologists more accurately titrate intravenous anesthetics for sedation and

analgesia in a critical care setting.11 There is growing interest in modeling the

interactions and effects of two or more anesthetics simultaneously. An increased

understanding of drug kinetics and effects will help anesthesiologists gain greater

control of their anesthetic.10 Thus models of these phenomenon may be useful in

optimizing the clinical care of patients, potentially offering guidance that may minimize

the time between the end of surgery and patient return to consciousness, reduce the

amount of anesthetics that are used, or more effectively prevent post-operative pain.

Kern et al. created response surfaces for propofol and remifentanil that describe

the drug effect in terms of surrogate measures (OAA/S, laryngoscopy, shin algometry,

and electrical tetany), shown in Figures 1.4.4,5 The models were developed using data

collected from 24 healthy volunteers. The results show a synergistic pharmacodynamic

interaction between remifentanil and propofol over the full clinical concentration range

and the stronger the noxious stimulus the stronger the interaction is between the drugs.

A population PD response surface represents the range of probabilities of

preventing a response to a stimulus at each drug concentration pair.21 A single isobole

represents all the drug concentration pairs that provide a specific probability in a given

population of preventing a response to a stimulus. However, it is difficult to assess

9

Figure 1.4 Response surface models for surrogate measures from Kern et al.. The top

left model represents the likelihood of an Observer’s Assessment of

Alertness/Sedation (OAA/S) score < 4. The top right model represents the

population’s likelihood of not responding to laryngoscopy. The bottom left and right

surfaces represents the percentage of maximum stimulus tolerated for shin algometry

and electrical tetany, respectively.

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graded levels of pain for individual patients under general anesthesia. In the operating

room, the anesthesiologist assesses surgical pain qualitatively—the patient either

responds to pain or does not. Thus, when individual patient data is plotted on the

population response surfaces, we are comparing individuals to a population. In other

words, a pharmacodynamic estimation does not directly predict whether a specific

patient will respond to a stimulus. Rather, if a patient responds to pain at a high

probability of anesthetic effect, then the patient can be characterized as being

pharmacologically resistant. A resistant patient will require higher dosing throughout

the surgery to provide sufficient anesthesia. The same drug regimen in a sensitive

patient might result in a prolonged time until recovery of consciousness.

Methods for Preliminary Study

In order to minimize clinical care, this observational study was structured to

have minimal impact upon the anesthesiologists’ and surgeons’ standard practice of

care. We observed moments of inadequate anesthesia throughout each surgical case,

indicated by a 20% rise in heart rate, blood pressure, or another somatic response. The

predicted Ceff during patient responses and at surgical landmarks (loss of

responsiveness, laryngoscopy, tracheal intubation, skin incisions, intraabdominal

manipulations, wound closure, skin closure, recovery of consciousness and extubation)

were then be plotted on the response surfaces created by Kern et al.4,5 The actual patient

responses were then compared to the likelihood of anesthesia as estimated by the

different response surfaces.

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The preliminary study, with institutional review board approval from the

University Hospital and informed consent involved seven patients with ASA physical

status I and II scheduled for laparoscopic surgery under total intravenous anesthesia. To

minimize experimental intrusiveness, a graduate student observer was the only

researcher present in the operating room. To collect dosing data, a laptop interfaced

with two Medfusion 3010a infusion pumps (Medex, Dublin, OH, USA) and a DocuJect

digital injectable drug monitor (DocuSys, Mobile, AL, USA). All boluses administered

through the DocuJect were flushed with a saline bolus to minimize the delay between

the recorded drug administration and the actual distribution of the drug to the effect-

site. To collect patient monitoring data, an A-2000 BIS EEG monitor (Aspect Medical

Systems, Newton, MA, USA) and CBM-7000, a continuous, non-invasive blood-pressure

monitor (Colin Medical Instruments Corp., San Antonio, TX, USA) also interfaced with

the laptop.

The digital drug dosing data, collected automatically, was used to run

pharmacokinetic simulations. The predicted drug concentrations at the times of surgical

landmarks were plotted on the relevant response surfaces of the surrogate measures.

Comparisons of the patient data to the pharmacodynamic predictions were to be used to

relate surrogate measures to surgical stimuli.

Conclusion from Preliminary Study

Several problems were initially encountered: logistically, it was difficult for an

individual to set up 2 patient monitors and 3 drug delivery systems. Clinically, the

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anesthesiologists were wary of relying on the Colin continuous non-invasive blood

pressure monitor for hemodynamic information and were unfamiliar with the DocuJect

bolus monitor combined with the saline flush necessitated by the study. Most

significantly, a first-year bioengineering graduate student lacked the clinical expertise to

reliably differentiate between patient responses to pain and responses to environmental

manipulations (such as when the patient was repositioned). After considering

preliminary results, it was decided that only observations of the loss of responsiveness,

the first attempt at laryngoscopy and tracheal intubation, the first skin incision, and the

recovery of responsiveness were to be compared to the surrogate measure surfaces of

sedation, laryngoscopy, shin algometry, and electrical tetany.

We developed a new protocol that involved more researchers, including clinical

research nurses, and fewer devices. The data-collecting laptop was interfaced to the

standard OR monitor, Datex AS/3 (Datex-Ohmeda Inc., Louisville, CO, USA), an A-2000

BIS, and two Medfusion 3010a infusion pumps. A 20% rise in heart rate (measured by

either the ECG or the BP cuff on the Datex AS/3) within one minute of a specific stimulus

was the primary indicator of a response to pain. Drug boluses were recorded by hand

instead of being digitally collected. Using this protocol, we collected data from 24

patients. This study is fully described in Chapter 2.

References

1. Schnider TW, Minto CF, Shafer SL, Gambus PL, Andresen C, Goodale DB, Youngs EJ:

The influence of age on propofol pharmacodynamics. Anesthesiology. 1999 Jun; 90

13

(6): 1502-16

2. Sheiner LB, Stanski DR, Vozeh S, Miller RD, Ham J: Simultaneous modeling of

pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin

Pharmacol Ther. 1979 Mar; 25 (3): 358-71

3. Scott JC, Cooke JE, Stanski DR.: Electroencephalographic quantitation of opioid

effect: comparative pharmacodynamics of fentanyl and sufentanil. Anesthesiology.

1991 Jan; 74 (1): 34-42

4. Kern SE, Xie G, White JL, Egan TE: Opioid-hypnotic synergy. Anesthesiology 2004

Jun; 100: (6): 1373-81

5. Xie G: Computer modeling and visualization of interaction between propofol and

remifentanil in volunteers using response surface methodology, Bioengineering. Salt

Lake City, University of Utah, 2001

6. Olofsen E, Nieuwenhuijs DJ, Sarton EY, Teppema LJ, Dahan A: Response surface

modeling of drug interactions on cardiorespiratory control. Adv Exp Med Biol.

2001; 499: 303-8

7. Struys MM, Vereecke H, Moerman A, Jensen EW, Verhaeghen D, De Neve N,

Dumortier FJ, Mortier EP: Ability of the bispectral index, autoregressive modelling

with exogenous input-derived auditory evoked potentials, and predicted propofol

concentrations to measure patient responsiveness during anesthesia with propofol

and remifentanil. Anesthesiology. 2003 Oct; 99 (4): 802-12

8. Bouillon T, Bruhn J, Radu-Radulescu L, Bertaccini E, Park S, Shafer S: Non-steady

state analysis of the pharmacokinetic interaction between propofol and remifentanil.

Anesthesiology. 2002 Dec; 97 (6): 1350-62

9. Bouillon T, Bruhn J, Radulescu L, Andresen C, Shafer TJ, Cohane C, Shafer S:

Pharmacodynamic interaction between propofol and remifentanil regarding

hypnosis, tolerance of laryngoscopy, bispectral index, and electroencephalographic

approximate entropy. Anesthesiology. 2004 Jun; 100 (6): 1353-72

10. Chernik DA, Gillings D, Laine H, Hendler J, Silver JM, Davidson AB, Schwam EM,

Siegel JL: Validity and reliability of the Observer’s Assessment of Alertness/Sedation

scale: study with intravenous midazolam. J of Clin Psychopharmacol 1990; 10 (4):

244-251

11. Minto C, Schnider T: Expanding clinical applications of population

pharmacodynamic modelling. Br J Clin Pharmacol. 1998 Oct; 46 (4): 321-33

14

12. Wakeling HG, Zimmerman JB, Howell S, Glass PSA: Targeting effect compartment

or central compartment concentration of PROP what predicts loss of consciousness?

Anesthesiology 1999; 90 (1): 92-97

13. Nava-Ocampo AA, Shafer SL, Velázquez-Armenta Y, Ruiz-Velazco S, Toni B:

Mathematical analysis of a pharmacodynamic model without plasma concentrations

to extend its applicability. Medical Hypotheses. 2003 60 (3): 453-57

14. Bailey JM, Shafer SL: A simple analytical solution to the three-compartment

pharmacokinetic model suitable for computer-controlled infusion pumps. IEEE

Transactions on Biomedical Engineering 1991; 38 (6): 522-25

15. Greco WR, Bravo G, Parsons JC: The search for synergy: a critical review from a

response surface perspective. Pharmacological Reviews 1995; 47 (2): 331-85

16. Berenbaum MC: Direct search methods in the optimization of cancer chemotherapy

regimens. Br J Cancer 1990 Jan; 61 (1): 101-9

17. Curatolo M, Schnider TW, Petersen-Felix S, Weiss S, Signer C, Scaramozzino P,

Zbinden AM: A direct search procedure to optimize combinations of epidural

bupivacaine, fentanyl, and clonidine for postoperative analgesia. Anesthesiology

2000 Feb; 92 (2): 325-37

18. Minto CF, Schnider TW, Short TG, Gregg KM, Gentilini A, Shafer SL: Response

surface model for anesthetic drug interactions. Anesthesiology 2000 Jun; 92 (6): 1603-

1616

19. Avram MJ, Krejcie TC: Using front-end kinetics to optimize target-controlled drug

infusions. Anesthesiology. 2003 Nov; 99 (5): 1078-86

20. Bjorkman S, Wada DR, Stanski DR: Application of physiologic models to predict the

influence of changes in body composition and blood flows on the pharmacokinetics

of fentanyl and alfentanil. Anesthesiology. 1998 Mar; 88 (3): 657-67

21. Short TG, Ho TY, Minto CF, Schnider TW, Shafer SL: Efficient trial design for eliciting

a pharmacokinetic-pharmacodynamic model-based response surface describing the

interaction between two intravenous anesthetic drugs. Anesthesiology. 2002 Feb; 96

(2): 400-08

CHAPTER 2

OBSERVATIONAL STUDY

Introduction

Pharmacokinetic (PK) models describe changes in anesthetic concentrations in

the body over time following drug administrations.1 Pharmacodynamic (PD) models

predict the level of anesthetic effect as a function of drug concentration.1 This

observational study combines a set of propofol and remifentanil pharmacokinetic and

pharmacodynamic models and evaluates how accurately they predict the level of

anesthesia in 24 patients undergoing abdominal laproscopic surgery. Trends to improve

differences between the model predictions and observations in the patients are identified

and discussed.

Kern et al. and Bouillon et al. created PD response surface models in healthy

volunteers using plasma samples, assayed drug concentrations, and surrogate measures

of drug effect.2,3 Mertens et al. created similar PD response surfaces in patients using

plasma samples, assayed drug concentrations, and clinical measures of drug effect.4

This study combines PK and PD models in an attempt to accurately predict

patient responses to clinical measures using drug dosing information but without

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assayed concentrations. Though it is not practical to measure the actual drug

concentrations in the brain, propofol and remifentanil effect-site concentrations, which

both act primarily in the central nervous system, can be predicted using

pharmacokinetic models.5 These models predict the concentrations in generalized

compartments as the drug is distributed throughout the body and is metabolized.6

Using these pharmacokinetic estimates, the pharmacodynamic models of Kern et al.

were compared to observations in patients for this study.2

We hypothesize that PK-PD combined models can accurately predict when a

patient loses and recovers responsiveness in the OR and whether a patient will respond

to laryngoscopy followed by tracheal intubation or to the first skin incision of surgery.

Further simulations were used to characterize the sensitivity of individual

pharmacokinetic and pharmacodynamic variables for these combined models.

Methods

Study Design

This observational study compares the predictions of combined pharmacokinetic

and pharmacodynamic (PK-PD) models in the operating room to observations of the

loss and recovery of responsiveness and of adequate anesthesia for two surgical

milestones: 1) laryngoscopy followed by tracheal intubation and 2) the first skin incision.

We collected intraoperative drug dosing information, observed the patient loss and

recovery of responsiveness, and recorded patient responses and non-responses to

surgical stimuli. Comparison of the PK-PD combined model predictions with the

17

patient observations was performed post hoc. Subsequent analyses of the parameters

for the PK-PD combined models were also performed.

Subjects and Apparatus

With institutional review board approval from the University of Utah Hospital

and informed consent of the patients, we studied 24, ASA physical status I, II, and III,

patients (11 males and 13 females) scheduled for abdominal laparoscopic surgery under

total intravenous anesthesia. All patients denied having cardiovascular, hepatic, or renal

disease or a history of alcohol or drug abuse. The intraoperative anesthetic regimen was

limited to propofol, remifentanil and fentanyl.

In the perioperative holding unit, a catheter was placed in the wrist of each

patient for fluid and drug administration. Two T-connectors (ET-04T Smallbore T-Port

Extension Set, B. Braun Medical Inc., Bethlehem, PA, USA) were attached to the

cannula, in-line with a Baxter IV drip set. Fluids were administered from the IV bag,

through IV tubing, through the two T-connectors, and into the patient’s vein.

Propofol and remifentanil syringes were loaded into separate infusion pumps

(Medfusion 3010a, Medex, Inc., Dublin, OH, USA). After the patient entered the OR, the

primed remifentanil and propofol infusion lines were attached to the two T-connectors

at the patient’s wrist to decrease any potential delays in drug delivery by minimizing the

tubing dead-space flushed by the IV drip. The anesthetists administered drug boluses

for both induction and maintenance through the second IV access port distal from the

patient while the IV was running. An intra-lab software interface collected data from the

18

two infusion pumps. A research nurse and a graduate student observer recorded drug

boluses given manually.

Observations at Clinical Milestones

The times of loss of responsiveness (LOR) and recovery of responsiveness (ROR)

were recorded by study investigators. LOR during induction was defined as when the

patient no longer responded to verbal commands or loudly calling his/her name. ROR

at the end of surgery was defined as when the patient responded to loud verbal

commands and gentle shaking.

Responses (and non-responses) to surgical stimuli of 1) laryngoscopy followed

by tracheal intubation (TI) and 2) the first skin incision (SI) were recorded by the

observers. A response to pain was characterized by a 20% increase in heart rate (within

1 minute of the stimulus) subjectively evaluated by the research nurse and the

anesthesiologist to be a reaction to a specific stimulus due to relatively light or

inadequate anesthesia. Somatic responses to noxious stimuli, such as movement or

tearing by the patient, were also considered “responses.”

Pharmacokinetic Modeling

The PK model estimates were calculated post-hoc using the patient and drug

dosing data. The pharmacokinetics of each drug were assumed independent of the

concentration of the other drugs. Each drug used a three-compartment plus effect-site

model.6 The difference equations used to iterate each model are shown in Equations 2.1,

2.2, 2.3, and 2.4.

19

dC1/dt = C2(t)*k21 + C3(t)*k31 + Ce(t)*ke0 - C1(t)*(k10 + k12 + k13 + k1e) +

Input(t) [2.1]

dC2/dt = C1(t)*k12 - C2(t)*k21 [2.2]

dC3/dt = C1(t)*k13 - C3(t)*k31 [2.3]

dCe/dt = C1(t)*k1e - Ce(t)*ke0 [2.4]

C1 , C2, C3, and Ce represent the concentrations in the central compartment, the fast

equilibrating peripheral compartment, the slow equilibrating peripheral compartment,

and the theoretical effect-site compartment, respectively. All compartment

concentrations are functions of time. The kxy represents the microrate constants of the

first-order drug transfer from compartment x to compartment y. We used the Minto-

Schnider parameters for remifentanil7 and an adapted Shafer et al. model for fentanyl8,9.

We used the Tackley model for propofol,10,11 since it had been used in the target-

controlled-infusion system used for building the PD models of Kern et al.2, and adapted

it to predict an effect-site concentration12. All the pharmacokinetic parameters are

shown in Table 2.1.

Pharmacodynamic Modeling

Kern et al. used four surrogate measures to predict anesthetic effects of sedation

and analgesia with an Emax model.2,13 They used a single surrogate for sedation; the

Observer’s Assessment of Alertness/Sedation (OAA/S) was used as a measure of the

20

Table 2.1. Parameters used for pharmacokinetic models. For our PK models, the lean

body mass (lbm) for males was defined as 1.1*mass - 128*(mass/height)2 and for

females as 1.07*mass - 148*(mass/height)2. Age is in years, mass in kilograms, and

height in centimeters.

Anesthetic Variable Value

Vc 5.1 - 0.0201(age - 40) + 0.072(lbm – 55)

k10 (2.6 - 0.0162 (age - 40) + 0.0191 (lbm – 55)) / (5.1 -

0.0201(age - 40) + 0.072(lbm - 55))

k12 (2.05 - 0.0301 (age - 40)) / (5.1 - 0.0201(age - 40) +

0.072(lbm - 55))

k13 (0.076 -0.00113(age - 40)) / (5.1 - 0.0201(age - 40) +

0.072(lbm - 55))

k21 (2.05 - 0.0301 (age - 40)) / (9.82 - 0.0811(age - 40) +

0.108 (lbm - 55))

k31 (0.076 -0.00113(age - 40)) / 5.42

Remifentanil

ke0 0.595 - 0.007(age - 40)

Vc 6.09

k10 0.0827

k12 0.471

k13 0.225

k21 0.102

k31 0.006

Fentanyl

ke0 0.112

Vc 0.320 * mass k10 0.0870 k12 0.1050 k13 0.0220 k21 0.0640 k31 0.00340

Propofol

ke0 0.250

21

depth of hypnosis.14 Three surrogate measures for analgesia were used; responses to

shin algometry, electrical tetany, and laryngoscopy were compared to patient responses

to the first skin incision and to laryngoscopy followed by tracheal intubation. The

general Emax model of Greco et al. for two synergistic drugs, used by Kern et al., is

shown in Equation 2.5 where Drugs A and B are the concentrations of the individual

drugs.13

1

γ

EC50B*EC50A

DrugB*DrugA*α

EC50B

DrugB

EC50A

DrugA

γ

EC50B*EC50A

DrugB*DrugA*α

EC50B

DrugB

EC50A

DrugA

Effect

+++

++=

[2.5]

EC50A and EC50B are the drug concentrations necessary to achieve 100% effect in

50% of the population using either drug alone. The α term describes the

pharmacodynamic synergism between drugs A and B while the γ term describes the

steepness of the surface or the pharmacodynamic variability within the population.

Although the OAA/S follows a discrete scale from 1 to 5, Kern et al. treated

OAA/S scores above and equal to 4 and below 4 as binary states of sedation, because an

OAA/S of 4 represents a sedation level comparable to the conscious sedation desired in

some surgeries. Using the raw data of Kern et al.,2,15 we calculated an additional

sedation response surface for the transition in OAA/S scores from 2 to 1 because an

OAA/S < 2 is similar to the states “sedated and non-responsive” in the OR for general

anesthesia. First EC50 values for propofol and remifentanil were fit (for each drug alone)

22

to a sigmoid Emax model using WinNonlin (Version 2.1, Pharsight Corp., Mountain

View, CA).2 Those individually solved EC50 values were plugged into the Greco et al.

interaction model to describe the synergistic effects of remifentanil and propofol on

sedation. The α and γ terms were solved using the least squares method in Excel (Excel

2000, Microsoft Corp., Redmond, WA). Table 2.2 shows the EC50, α, and γ values for the

surrogate measures of OAA/S < 2, Laryngoscopy, Shin Algometry, and Electrical Tetany.

The effects of the anesthetics are estimated as functions of a pair of propofol and

remifentanil concentrations. Thus, expected PD effects were calculated using the PK

estimates of the drug Ceff at the time of LOR, ROR, TI, and SI. To account for the

analgesic effect of fentanyl we assumed its relative opioid effect to be 1.2.16 To calculate

the total opioid concentration, normalized to remifentanil, the predicted concentrations

of fentanyl were multiplied by their relative opioid effect (1.2) and were added to the

predicted concentration of remifentanil. We estimated the PD effect from the total

opioid Ceff and the propofol Ceff; the total opioid Ceff values and propofol Ceff values at

LOR, ROR, TI, or SI marked points on the response surfaces.

Data Analysis

We used the estimated Ceff values at the time of LOR, ROR, TI, and SI to calculate

the PD prediction of the effects of propofol and total opioid. These predictions were

compared to the observations of the patient at these milestones. For LOR and ROR, we

also used the observations 30 seconds prior to the recorded change in sedation state. For

example, all observations of patients at LOR were “unresponsive” but each of

23

Table 2.2. Parameters used for pharmacodynamic models. EC50Prop is in μg/ml, EC50Remi

is in ng/ml, and α and γ terms are both unitless.

Surrogate Measure Variable Value

EC50Prop 2.60

EC50Remi 34.0

α 6.34 OAA/S < 2

γ 5.51

EC50Prop 5.60

EC50Remi 2.20

α 33.2 Laryngoscopy

γ 2.20

EC50Prop 4.16

EC50Remi 8.84

α 8.20 Shin Algometry

γ 8.34

EC50Prop 4.57

EC50Remi 21.3

α 14.7 Electrical Tetany

γ 6.00

24

those patients were “responsive” 30 seconds earlier. To visualize the PD predictions at

the clinical milestones, we used Matlab (The MathWorks, Inc., v 6.5, release 13, Natick,

MA, USA) to plot these values on the PD response surfaces described by Equation 2.5

using parameter sets from Table 2.1.

Combined Model Sensitivity Analysis

To identify the most sensitive parameter of the PK-PD combined models, several

simulations were run using individually scaled parameter values. The differences

between the initial model predictions (a continuous variable between 0 and 1) and the

observations of the patients (where 0 is a response and a non-response is 1) at each

stimulus were calculated, squared, and summed.17 As the model parameters were

scaled (independently of each other), we summed the square of the differences between

these new predictions and the observations. PD changes were based on unchanged PK

simulations, and PK changes were compared to unchanged PD simulations. Preliminary

analysis identified k10 and Vc as the two most influential PK parameters. An initial set of

four different scaling factors (1⁄10, ½, 2, and 10) were applied to k10, Vc, EC50Prop, EC50Remi, α,

and γ, individually. A new set of scaling factors were individually chosen for EC50Prop,

EC50Remi, and α to lessen the total squared differences. This process was iterated a total of

three times to observe trends in the relative sensitivity of the prediction error to

individual parameters.

Results

All patients enrolled completed the study. The 24 patients had a mean age of

25

38.9 ± 12.4 years, weight of 86.4 ± 22.6 kg, and height of 171.58 ± 8.99 cm. Fifteen of the

cases were laproscopic cholecystectomies, 6 were laproscopic hernia repairs, and 3 were

laproscopic nissen fundoplications. Sixteen of the anesthetics were delivered by 2

experienced CRNAs, two by two third year residents, three by two second year residents

and three by a first year resident.

All but two patients received midazolam (average dose of 1.61 mg, ± 0.49) prior

to entering the operating room (OR). (One of the patients declined midazolam and

another patient received midazolam after arriving in the OR.) Propofol was the only

other intraoperative sedative. Remifentanil and fentanyl were given during the surgial

procedure. 10 patients received 30 mg ketorolac tromethamine late in the procedures for

maintenance and post-operative pain management, however these pharmacokinetics

were not modeled.

Table 2.3 gives the average predicted Ceff values at the observed LOR, ROR, TI,

and SI. The table also indicates the number of patients who responded within 1 minute

of laryngoscopy followed by tracheal intubation or of the first skin incision.

Figures 2.1, 2.2, 2.3, 2.4, and 2.5 show Ceff values of propofol and remifentanil at

LOR, ROR, TI, and SI plotted on the Kern et al. OAA/S < 2, Laryngoscopy, and Electrical

Tetany PD response surfaces. The response surfaces are shown from a topographical

(top-down) perspective where the darker shading represents lower likelihoods of

anesthesia and thus higher likelihoods of patient responses. The 50% and 95% isoboles

are also shown on each surface. The figures show Ceff values over 60 seconds; the Ceff

prior to the event is the “tail” (triple triangles) and the Ceff 30 seconds following the

26

Table 2.3. Observations and pharmacokinetic Ceff estimates at surgical milestones.

Surgical Stimulus n Total Opioid (ng/ml)

Propofol (µg/ml)

Observed Loss of Responsiveness 23 6.83±2.19 1.00±0.91

Observed Return of Responsiveness 23 2.83±1.59 1.95±0.42

13 NR° 6.96±1.86 2.66±0.86 Laryn. And Tracheal Intub.

11 R† 5.81±1.45 2.31±0.64

23 NR 5.90±1.94 2.82±0.66 First Skin Incision

1 R 4.23 1.57

° NR indicates patients who did not respond to pain at the surgical milestone. † R indicates patients who responded to pain at the surgical milestone.

27

Figure 2.1. Ceff values at loss of responsiveness on the sedation response (OAA/S<2).

The large circles represent the remifentanil and propofol Ceff values (predicted by the

pharmacokinetic models) at LOR when the patients are sedated. The squares

represent estimated Ceff values 30 seconds prior to LOR when the patients were not

sedated. The “arrows” show the PK model-predicted Ceff values 30 seconds prior to

and after LOR.

28

Figure 2.2. Ceff values at recovery of responsiveness on the sedation response surface

(OAA/S<2). The large squares represent the remifentanil and propofol Ceff values

(predicted by the pharmacokinetic models) at ROR. The circles show estimated Ceff

values 30 seconds prior to ROR when the patients were sedated. The changes in Ceff

from 30 seconds before ROR to 30 seconds after ROR are minimal.

29

Figure 2.3. Ceff values at laryngoscopy followed by tracheal intubation on the

response surface for laryngoscopy. Stars represent patient responses and circles

represent patient non-responses at the remifentanil and propofol Ceff values

(predicted by the pharmacokinetic models) at TI. The arrows show the PK model-

predicted Ceff values for 30 seconds prior to TI and the Ceff values 30 seconds after TI.

30

Figure 2.4. Ceff values at the first skin incision on the response surface for shin

algometry. The star represents the only patient response to the first skin incision and

the circles represent patient non-responses at the remifentanil and propofol Ceff

values (predicted by the pharmacokinetic models) at SI. The arrows show the PK

model-predicted Ceff values for 30 seconds prior to SI and the Ceff values 30 seconds

after SI.

31

Figure 2.5. Ceff values at the first skin incision on the response surface for electrical

tetany. The star represents the only patient response to the first skin incision and the

circles represent patient non-responses at the Ceff values (predicted by the

pharmacokinetic models) at SI. The arrows show the PK model-predicted Ceff values

for 30 seconds prior to SI and the Ceff values 30 seconds after SI.

32

actual observation is marked as the “head” (single triangle).

Table 2.4 shows the summed squared differences between the model predictions

and the observations of the patients. To observe the sensitivity of the parameters, they

were individually scaled. Some of the scaling factors that resulted in an improved fit

between the predictions and observations and the summed squared differences are also

shown. Due to a lack of patient responses to the first skin incision, evaluation of the

sensitivity of models predicting analgesia for skin incision (shin algometry and electrical

tetany) was not performed.

Discussion

We postoperatively used intraoperative dosing data to calculate PK predictions

for propofol, remifentanil, and fentanyl at the times of surgical milestones. These PK

predictions were then used to create PD predictions of patient responses at these

moments. These PD predictions were compared to observations in the patients and

were plotted on PD response surface models of surrogate measures. In this

observational study, we found great variance in the data from the operating room and

were unable to indisputably relate surrogate measures to clinical measures. However,

by scaling individual parameters, we recognized several trends that may allow for

future improvement of model predictions.

From the LOR plot Figure 2.1, we see that only about 1/3 of the changes from

“responsive” to “unresponsive” occurred at PD prediction levels about the 50% isobole.

33

Table 2.4. Summed squared differences for scaled PK-PD model parameters. Scaling

factors were applied only to the parameter indicated, while original values (Tables 2.1

and 2.2) were used for all other parameters.

Model and Stimulus Parameter Improving Scaling

Factor Summed Squared

Difference

All Initial Values 12.5

k10 0.1 9.46

Vc 0.5 9.13

EC50Prop 0.5 8.18

EC50Remi 0.5 7.31

α 2.5 7.70

OAA/S < 2 and LOR

γ 0.15 9.86


k10 1 15.8

Vc 1 15.8

EC50Prop 1.3 14.6

EC50Remi 3.0 14.0

α 0.15 13.9

OAA/S < 2 and ROR

γ 0.05 11.5


k10 2.0 7.32

Vc 2.0 7.35

EC50Prop 2.1 7.25

EC50Remi 3.0 7.01

α 0.25 6.94

Laryngoscopy (followed by Tracheal Intubation)

γ 0.06 5.96

Shin Algometry and Skin Incision All Initial Values 24.0

Electrical Tetany and Skin Incision All Initial Values 23.7

34

For an ideal fit, half of those points would fall above and half below the 50% isobole. An

improved fit occurs when each parameter (EC50Prop, EC50Remi, α, γ) is scaled < 1, except for

the α term. The low γ scaling factor is most easily interpreted as the large variance

within the data.

While this suggests that the “actual” drug effect was greater than predicted by

the PD models or that “real” Ceff drug concentrations were higher than those predicted,

our data does not let us differentiate between these two possibilities. Other PK errors

might arise from a misspecified PK model that eliminates the drug too quickly or that

predicts too low of a peak drug concentration. Differences between predicted and real

Ceff may be due to a time lag between the injection of a drug and its distribution to the

effect site. Wada and Ward suggested using fixed input and recirculation delays

between the infusion and the estimated change in PK model plasma concentration

predictions.18 Had we assumed a 30 second distribution delay, our data points would

shift to higher drug concentrations such that ¾ of the changes from “responsive” to

“unresponsive” occurred at PD prediction levels about the 50% isobole. A further

limitation of our PK model is that it was designed assuming instantaneous and complete

mixing, a fixed Vc, and did not account for PK interactions between propofol,

remifentanil, or fentanyl, nor the effects of drug recirculation. 19,20,21 Thus studying LOR

by bolus induction, as seen in the majority of our study population, is especially

difficult.

It is also noteworthy that the average remifentanil Ceff at LOR (6.83 ng/ml) is 20%

of EC50Remi (34.0 ng/ml) , while the average propofol Ceff at LOR (1.00 μg/ml) is 38% of

35

EC50Prop (2.60 μg/ml). This indicates that for these patients and these PD models,

propofol contributed more to sedation than did remifentanil.

Though the patient transitions from “unresponsive” to “responsive,” shown in

Figure 2.2, are not evenly distributed above and below the 50% isobole, the majority of

these transitions are between the 50% and 95% isoboles. This suggests that the overall

combined model predictions are relatively close to matching the patients studied. The

simulations for ROR corroborate that at relative steady state, the PK models7,8,9,10,11

studied are well tuned. This is expected towards the end of surgery when the

pharmacokinetics are more stable resulting in Ceff values close to plasma concentrations.

Furthermore, the average remifentanil Ceff at ROR (2.83 ng/ml) is 8% of EC50Remi (34.0

ng/ml) , while the average propofol Ceff at ROR (1.95 μg/ml) is 75% of EC50Prop (2.60

μg/ml). In this study, propofol controlled sedation more than remifentanil. However,

for ROR, we observed less synergism or less drug potency than our PD models

predicted. Clinically, these data suggest that to achieve quicker wake-ups while

managing pain, higher levels of opioid may be acceptable as sedative levels decrease.

Although sedation is treated as a binary state in the operating room, it is actually

a continuous measure. In our study, this discrepancy is compounded by our inability to

identify the exact moments of LOR and ROR. In a controlled clinical study

environment, a typical OAA/S is used in which a volunteer may be asked to repeat a

phrase multiple times per minute in order to observe the exact moment of LOR and

ROR.2 Doufas et al. has used another monitor, the automated responsiveness test (ART)

or automated responsiveness monitor (ARM), to record the moment of sedation.22,23,24 In

36

contrast, in the operating room, the moment of LOR was assessed by a research nurse

watching the patient and the anesthesiologist but without directly addressing the

patient. An automated system that requires a response from the patient may provide a

more consistent and precise measure of LOR and ROR.22,23,24

For TI, it appears that the drug effect was less than predicted, or else that higher

concentrations are necessary for providing “adequate anesthesia.” Without drug

concentration assays, it is impossible to distinguish whether this is due to kinetics or due

to dynamics. Clinically, the anesthetists wait until they expect the peak

pharmacodynamic effect to be achieved prior to performing laryngoscopy and tracheal

intubation. Nonetheless, as shown in Figure 2.3, nearly half the patients responded to

tracheal intubation (following laryngoscopy). Tracheal intubation followed

laryngoscopy as quickly as possible. As a result, we were unable to separate these two

milestones and treated them as a single stimulus. That so many patients responded to

laryngoscopy followed by tracheal intubation while at predicted effect levels above the

95% isobole of the laryngoscopy response surface is not surprising; we expect tracheal

intubation followed by laryngoscopy is more stimulating than tracheal intubation alone.

Merten et al. found similar results, creating separate response surfaces for laryngoscopy

alone and laryngoscopy followed by tracheal intubation.4 However, it appears that less

synergism was observed than was predicted by the PD model of Kern et al. for

laryngoscopy. Clinically, higher drug concentrations, compared to those targeted for

laryngoscopy alone, are necessary for tracheal intubation.

Because only a single patient responded to skin incision, we cannot draw strong

37

conclusions regarding the predictiveness of the PK and PD combined models. However,

it is noteworthy that on the electrical tetany response surface, the single response was

predicted by the PK and PD models to be near the 50% isobol while the rest of the

patients were at or above the 95% isobol at this surgical milestone (see Figure 2.5). The

data did not fit the shin algometry response surface as conveniently. Although we

observed the first skin incision clearly, the second, third, fourth, etc. incisions were less

obvious and were not consistent between the different types of surgery. Furthermore

evaluating repeated stimuli in the same patient violates a fundamental assumption of

independence, necessary for most statistical tests. However, if repeated measures were

used, titration throughout the surgery may result in a better evaluation of intraoperative

PD models of repeated surgical stimuli, such as skin incisions or wound closures. A

similar scheme was successfully used by Mertens et al. to create a laryngoscopy

response surface directly from patient data.4

A fundamental challenge for this study was the degrees for freedom we allowed

while considering numerous variables. The anesthetists were only asked to follow their

(individual) standard practices to provide total intravenous anesthesia (TIVA) using

propofol and remifentanil as the primary anesthetic agents. In other words, we did not

control the drug concentration ranges.2,4,25 This lack of control was exasperated by a lack

of plasma samples that would be necessary to separate PK from PD errors.

Future protocols should require a slow induction by infusion to minimize the

differences between bolus and infusion pharmacokinetics and dynamics. This slower

induction may increase the accuracy of the pharmacokinetic predictions by minimizing

38

the bolus kinetics that are particularly hard to predict. Greater control might be

accomplished by prescribing an induction scheme and specific dosing changes in

response to observations in patients.

Abdominal laproscopic surgeries were chosen because they were appropriate for

propofol and opioid TIVAs, and because they were common in the University Hospital.

However, we were unprepared for the subtle stimulation differences between

cholecystectomies, hernia repairs, and nissen fundoplications. For example, a bougie

was nasally inserted for nissen fundoplications and staples were used for some hernia

repairs, and some surgeries were finished within an hour while some required three

hours. We chose the four clinical milestones of LOR, ROR, TI, and SI because they were

consistently identifiable for all these TIVA-appropriate surgeries. Had we observed

more patients for the same types of surgeries, we would expect to have reported on the

PK-PD combined model predictions for other specific surgical stimuli, such as responses

to internal sutures or staples for hernia repairs, the incisions and removal of the gall

bladder for cholecystectomies, or insertion of a Bougie tube for fundoplications.

In summary, the PK-PD combined models do not predict responsiveness to

laryngoscopy followed by tracheal intubation. It is likely that the surgical stimulus is

more painful than the surrogate measure. For LOR and ROR, 22% and 65% of the data

points from the PK-PD combined models for OAA/S < 2 fall between the 50% and 95%

isobols, respectively. That the average propofol Ceff for the OR data for LOR and ROR is

closer to EC50Prop than the remifentanil Ceff is to EC50Remi suggests that propofol (rather

than remifentanil) was the main contributor to responsiveness in these patients. This

39

suggests that to help manage pain postoperatively while having a quick recovery of

consciousness, opioid levels should be maintained while propofol levels should be

reduced.

References




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CHAPTER 3

CONCLUSION

Summary

The aim of the study was to evaluate how well combined PK and PD models

predict the depth of anesthesia in patients by modeling sedation and analgesia to

specific stimuli. By study design, observations were made without taking plasma

samples and without changing the practice of the surgeon and the anesthesiologist. We

hoped to find a clear pharmacodynamic relationship between surgical stimuli and

surrogate measures. Ultimately, we identified trends in how to adjust population

pharmacological models to provide predictions that better match observations in the

study patient population. Clinically these trends suggest how greater analgesia with

quicker wake-ups can be achieved with propofol and opioids.

Comparison of Observational Studies and Clinical Studies

It is clear that a clinical research study in which anesthetic regimens are

controlled, can validate pharmacokinetic and pharmacodynamic models more robustly

than an observational study.1 In this observational study, surgical stimuli occurred at

the convenience and discretion of the clinicians, not at specific steady-state

43

concentrations. In contrast, in a clinical research study in volunteers, specific anesthetic

concentrations were targeted and maintained before applying a surrogate stimulus.

Thus, in a clinical research study, it is relatively easy to compare consistent stimuli at

preset anesthetic concentrations2,3,4,5 while neither the stimuli nor the concentrations are

consistent in an observational study6. This is one of the reasons that the relationships

between surrogate measures and surgical stimuli remain unclear.

Though this observational study ultimately did not define the relationships

between surrogate measures and surgical stimuli, the parameters most useful for tuning

PK and PD models were identified by post hoc simulations. However, without assayed

anesthetic concentrations, it is impossible to identify “real” PK and PD parameters for

the 24 patients studied. Nonetheless, these results help provide a clinical rational for

anesthetic recipes: propofol is the key anesthetic in providing sedation while opioids,

potentiated even by low propofol concentrations, provide analgesia.

Utility and Limitations of Clinical Pharmacological Modeling

Pharmacological models can be used to calculate the anesthetic path for the

shortest wake-up times while avoiding other negative side effects. There are two main

obstacles for bringing this information to the operating room for clinical use: 1) there are

few tools that provide modeled information to the clinician and 2) models are usually

population based. Target-controlled infusion pumps use PK models to achieve and

maintain clinician-selected concentrations. However these systems do not truly target

the anesthesiologists’ primary interest: appropriate sedation and analgesia for their

44

patients. Future efforts will include the real-time calculation and visualization of both

PK and PD models in the operating room.

Syroid et al.7 has shown that the visualization of pharmacokinetic and

pharmacodynamic models in a simulation scenario allows the anesthesiologist to exert

greater control over the anesthetic. This can result in using less drug and achieving

quicker wake-ups. In other cases, visualization may help clinicians identify dosing

errors. Using models to help titrate the anesthetic may lead to safer anesthesia.

A current limitation for the use of pharmacokinetic and pharmacodynamic

models is that they are generally population-based.8 Yet in the operating room,

individualized models would provide more accurate predictions for each patient. The

use of feedback controllers and microassays may allow future adaptation of population

models to an individual patient.

Future Work

Future work will focus on collecting data from surgeries where the surgical

stimuli are very consistent. For the current study, we observed several different

abdominal laproscopic surgeries but future studies should focus on surgeries where the

stimuli and the surgical procedure are more uniform. The anesthetic plan should be

defined pre-operatively to include a slow induction and titrating the anesthetic while

maintaining patient comfort.3 Plasma samples should be taken to separate

pharmacokinetic from pharmacodynamic prediction errors.8,9 Additionally, the

interactions between other anesthetics should be studied. Ultimately, the goal will be to

45

visualize pharmacological models in real-time to guide the anesthesiologist to a specific

predicted level of anesthesia for a variety of stimuli with a library of anesthetics.

References

1. Fisher DM, Wright PMC: Are plasma concentration values necessary for

pharmacodynamic modeling of muscle relaxants? Anesthesiology 1997 Mar; 86 (3):

567-75

2. Bruhn J, Bouillon TW, Radulescu L, Hoeft A, Bertaccini E, Shafer SL: Correlation of

approximate entropy, bispectral index, and spectral edge frequency 95 (SEF95) with

clinical signs of “anesthetic depth” during coadministration of propofol and

remifentanil. Anesthesiology 2003 Mar; 98 (3): 621-7

3. Mertens MJ, Olofsen E, Engbers FHM, Burm AGL, Bovill JG, Vuyk J: Propofol

reduces perioperative remifentanil requirements in a synergistic manner: response

surface modeling of perioperative remifentanil-propofol interactions.

Anesthesiology 2003; 99 (2): 347-359




approximate entropy. Anesthesiology 2004 Jun; 100 (6): 1353-72


Jun; 100: (6): 1373-81

6. Vuyk J, Lim T, Engbers FH, Burm AG, Vletter AA, Bovill JG: The pharmacodynamic

interaction of propofol and alfentanil during lower abdominal surgery in women.

Anesthesiology 1995; 83 (1): 8-22

7. Syroid ND, Agutter J, Drews FA, Westenskow DR, Albert RW, Bermudez JC, Strayer

DL, Prenzel H, Loeb RG, Weinger MB: Development and evaluation of a graphical

anesthesia drug display. Anesthesiology 2002; 96: 565-575



9. Vuyk J, Engbers FHM, Burm AGL, Vletter AA, Bovill JG: Performance of computer-

controlled infusion of propofol: an evaluation of five pharmacokinetic parameter

sets. Anesth Analg 1995; 81: 1275-82

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COMBINING PROPOFOL AND REMIFENTANIL … · combining propofol and remifentanil pharmacokinetic and pharmacodynamic models in the operating room: an observational study by farrant